Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve problems of character recognition, but their use has become as widespread as it is now thanks to recent researches. These CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
The CNNs are widely used in a field of autonomous driving and they detect obstacles by analyzing images obtained through a camera attached to a vehicle, and detect free space, etc., so that the vehicle can be safely driven.
However, a method of allowing the autonomous driving of the vehicle using the CNNs as described above may be difficult to perform in a specific situation. In other words, the CNNs must use the images obtained through the camera, but if the images are shaken due to a camera shake or the like, then a jittering effect occurs in which the images change greatly for each frame of the images. If such jittering occurs, the CNNs cannot track the movement of objects on the images easily. Especially, this jittering effect occurs very frequently in the autonomous driving of military vehicles which frequently travel off-road, and a method for preventing this is very important.
As a conventional technique for preventing such jittering, there is a method of physically correcting the jittered images by measuring the movement of the camera, but, in this case, the weight of the camera module is increased, the cost thereof is expensive, and use of multiple devices increases the risk of malfunction. Although a software technique rather than the above-mentioned physical technique exists as a conventional technique, it takes into consideration only the optical flow of an object of an image, which causes a problem of generating a large image distortion in the process of correcting the image in which jittering occurs.